Section 2: Sticky information

Sticky information gives some a major cost advantage over others in the case of many innovation opportunities. For example, users simply know much more about their needs than do producers – and manufacturers cannot economically acquire that information when it is “sticky.” Earlier work on this topic tended to focus on tacit information – which is a subset of sticky information.

Abstract: To solve a problem, needed information and problem-solving capabilities must be brought together. Often the information used in technical problem solving is costly to acquire, transfer, and use in a new location — is, in our terms, “sticky.” In this paper we explore the impact of information stickiness on the locus of innovation-related problem solving. We find, first, that when sticky information needed by problem solvers is held at one site only, problem solving will be carried out at that locus, other things being equal. Second, when more than one locus of sticky information is called upon by problem solvers, the locus of problem solving may iterate among these sites as problem solving proceeds. When the costs of such iteration are high, then, third, problems that draw upon multiple sites of sticky information will sometimes be “task partitioned” into subproblems that each draw on only one such locus, and/or, fourth, investments will be made to reduce the stickiness of information at some locations. Information stickiness appears to affect a number of issues of importance to researchers and practitioners. Among these are patterns in the diffusion of information, the specialization of firms, the locus of innovation, and the nature of problems selected by problem solvers.

Abstract: The unit cost of producing manufactured goods has been shown to decline significantly as more are produced. It has been argued that [`]learning by doing’ is at the root of this phenomenon, but the modes of learning actually involved have not been studied in detail. In this paper we attempt to provide a better understanding of the learning behaviors involved in learning by doing via a study of 27 problems that affected two novel process machines in their first years of use in production.
First, [`]interference finding,’ is described, a form of learning by doing that appears to be central to the discovery of the problems studied. Next, the reasons why the problems identified by templating were not discovered prior to field use – before [`]doing’ – are explored. Two causes are identified: an inability to identify existing problem-related information in the midst of complexity, and the introduction of new problem-related information by users and other problem solvers who learn by doing after field introduction of the machine. We find that problems due to information lost in complexity emerge earlier than do problems due to user learning by doing. Tests of reason are used to show why it would be very difficult to eliminate doing from learning by doing. Finally, other implications of the study findings are discussed.

Abstract: This paper explores the nature of adaptive learning around new technology in organizations. To understand this issue, we examine the process of problem solving involving new production equipment during early factory use. We find that adaptation is a situated process, in that different organizational settings (1) contain different kinds of clues about the underlying issues, (2) offer different resources for generating and analyzing information, and (3) evoke different assumptions on the pan of problem solvers. Consequently, actors frequently must move in an alternating fashion between different organizational settings before they can identify the causal underpinnings of a problem and develop a suitable solution. These findings suggest that traditional, decontextualized theories of adaptive learning and of collaboration could be improved by taking into account that learning occurs through people interacting in context–or, more specifically, in multiple contexts. Learning is often enhanced not just by bringing people together but by moving them around to confront different sorts of clues, gather different kinds of data, use different kinds of tools, and experience different pressures relevant to a given problem. We discuss both managerial and theoretical implications of these findings.

Abstract: Those who solve more of a given type of problem tend to get better at it–which suggests that problems of any given type should be brought to specialists for a solution. However, in this paper we argue that agency-related costs and information transfer costs (“sticky” local information) will tend drive the locus of problem-solving in the opposite direction–away from problem-solving by specialist suppliers, and towards those who directly benefit from a solution and who have difficult-to-transfer local information about a particular application being solved, such as the direct users of a product or service. We examine the actual location of design activities in two fields in which custom products are produced by “mass-customization” methods: application-specific integrated circuits (ASICs) and computer telephony integration (CTI) systems. In both, we find that users rather than suppliers are the actual designers of the application-specific portion of the product types examined. We offer anecdotal evidence that the pattern of user-based customization we have documented in these two fields is in fact quite general, and we discuss implications for research and practice.

Abstract: In a study of innovations developed by mountain bikers, we find that user-innovators almost always utilize “local” information – information already in their possession or generated by themselves – both to determine the need for and to develop the solutions for their innovations. We argue that this finding fits the economic incentives operating on users. Local need information will in general be the most relevant to user-innovators, since the bulk of their innovation-related rewards typically come from in-house use. User-innovators will increasingly tend to rely on local solution information as the stickiness of non-local solution information rises. When user-innovators do rely on local information, it may be possible to predict the general nature of the innovations they might develop.